Funding for this project was provided in part by the Defense Health Program (HU0001-16-2-0006 and HU0001-16-2-00014) to the Uniformed Services University for the Gynecologic Cancer Center of Excellence. The sponsors had no role in the design, execution, interpretation, or writing of the study. Disclaimer: The views expressed herein are those of the authors and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.

<ol><li>Motohara, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+evolving+story+of+the+metastatic+voyage+of+ovarian+cancer+cells%3A+cellular+and+molecular+orchestration+of+the+adipose-rich+metastatic+microenvironment%5BTitle%5D)+AND+%22Oncogene%22%5BJournal%5D)">An evolving story of the metastatic voyage of ovarian cancer cells: cellular and molecular orchestration of the adipose-rich metastatic microenvironment.</a> Oncogene. 38 (16), 2885-2898 (2019).</li>
<li>Aran, D., Sirota, M., Butte, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Systematic+pan-cancer+analysis+of+tumour+purity%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">Systematic pan-cancer analysis of tumour purity.</a> Nature Communications. 6, 8971(2015).</li>
<li>Hunt, A. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Extensive+three-dimensional+intratumor+proteomic+heterogeneity+revealed+by+multiregion+sampling+in+high-grade+serous+ovarian+tumor+specimens%5BTitle%5D)+AND+%22iScience%22%5BJournal%5D)">Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens.</a> iScience. 24 (7), 102757(2021).</li>
<li>Dou, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Proteogenomic+characterization+of+endometrial+carcinoma%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Proteogenomic characterization of endometrial carcinoma.</a> Cell. 180 (4), 729-748 (2020).</li>
<li>Zhang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrated+proteogenomic+characterization+of+human+high-grade+serous+ovarian+cancer%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Integrated proteogenomic characterization of human high-grade serous ovarian cancer.</a> Cell. 166 (3), 755-765 (2016).</li>
<li>Gillette, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Proteogenomic+characterization+reveals+therapeutic+vulnerabilities+in+lung+adenocarcinoma%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma.</a> Cell. 182 (1), 200-225 (2020).</li>
<li>Silvestri, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein+pathway+biomarker+analysis+of+human+cancer+reveals+requirement+for+upfront+cellular-enrichment+processing%5BTitle%5D)+AND+%22Laboratory+Investigation%22%5BJournal%5D)">Protein pathway biomarker analysis of human cancer reveals requirement for upfront cellular-enrichment processing.</a> Laboratory Investigation. 90 (5), 787-796 (2010).</li>
<li>Echle, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Deep+learning+in+cancer+pathology%3A+a+new+generation+of+clinical+biomarkers%5BTitle%5D)+AND+%22British+Journal+of+Cancer%22%5BJournal%5D)">Deep learning in cancer pathology: a new generation of clinical biomarkers.</a> British Journal of Cancer. 124 (4), 686-696 (2021).</li>
<li>Lee, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+analysis+of+clinically+defined+subsets+of+high-grade+serous+ovarian+cancer%5BTitle%5D)+AND+%22Cell+Reports%22%5BJournal%5D)">Molecular analysis of clinically defined subsets of high-grade serous ovarian cancer.</a> Cell Reports. 31 (2), 107502(2020).</li>
<li>Xuan, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Standardization+and+harmonization+of+distributed+multi-center+proteotype+analysis+supporting+precision+medicine+studies%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies.</a> Nature Communications. 11 (1), 5248(2020).</li>
<li>Hughes, C. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantitative+profiling+of+single+formalin+fixed+tumour+sections%3A+proteomics+for+translational+research%5BTitle%5D)+AND+%22Scientific+Reports%22%5BJournal%5D)">Quantitative profiling of single formalin fixed tumour sections: proteomics for translational research.</a> Scientific Reports. 6 (1), 34949(2016).</li>
<li>Espina, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+portrait+of+tissue+phosphoprotein+stability+in+the+clinical+tissue+procurement+process%5BTitle%5D)+AND+%22Molecular+%26+Cellular+Proteomics%22%5BJournal%5D)">A portrait of tissue phosphoprotein stability in the clinical tissue procurement process.</a> Molecular & Cellular Proteomics. 7 (10), 1998-2018 (2008).</li>
<li>Espina, V., Heiby, M., Pierobon, M., Liotta, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Laser+capture+microdissection+technology%5BTitle%5D)+AND+%22Expert+Review+of+Molecular+Diagnostics%22%5BJournal%5D)">Laser capture microdissection technology.</a> Expert Review of Molecular Diagnostics. 7 (5), 647-657 (2007).</li>
<li>Havnar, C. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Automated+dissection+protocol+for+tumor+enrichment+in+low+tumor+content+tissues%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%22%5BJournal%5D)">Automated dissection protocol for tumor enrichment in low tumor content tissues.</a> Journal of Visualized Experiments. (169), e62394(2021).</li>
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T.P.C. is a ThermoFisher Scientific, Inc SAB member and receives research funding from AbbVie.

While there have been multiple study precedents aimed at developing and/or improving workflows for enrichment of target cellular subpopulations from FFPE and/or fresh-frozen tissues and methodologies for maintaining sample quality during processing9,12,13,14,15, a substantial need exists to develop automated strategies for preparing clinical tissue specimens for molecular analyses to decrease variability and increase reproducibility. This workflow describes a standardized, semiautomated protocol that integrates existing image analysis software tools (see the Table of Materials) for histology-resolved harvest of discrete cell populations by LMD from clinical tissue specimens.
Spatially-resolved LMD enrichment of ROIs capturing discrete cell populations represents a next-generation tissue processing step prior to multiomic analyses to improve molecular characterization and identification and facilitate cell-selective biomarker discovery. This protocol improves upon existing methodologies by reducing the often-long exposure of tissue sections to the ambient environment that is associated with manual segmentation of ROI by a histologist (which can take &#62;1-2 h prior to the LMD collection). This workflow instead allows ROI to be preidentified by AI-guided classification and segmentation. Limiting tissue dwell time will decrease spurious variations in the assessments of highly labile molecular targets, such as phosphopeptides and mRNA, or for antibody-based analytical techniques that rely on a target protein being in its native conformation for detection.
Cutting neat calibrator fiducials onto the PEN membrane slide that are clearly visible in the scanned slide image is one of the key components enabling integration of the image analysis software (see the Table of Materials) with the LMD workflow. Ensuring that the calibrators have a precise (&#34;clean&#34;) point at the bottom of the &#34;V&#34; shape allows for selection of a precise point in the image analysis software for the calibrator lines to be drawn from, as described in steps 5.1.6 and 5.2.13. Alignment of these points during import into the LMD software is critical for properly overlaying the annotations (facilitated through the generation of a compatible .xml file using the &#34;Malleator&#34; and/or &#34;Dap&#7885;&#34; algorithms) onto the relevant tissue ROI on the physical LMD slide. It is necessary to highlight all shapes and collectively &#34;drag and drop&#34; into place even when the alignment is precise upon import into the LMD software to register the vertical (z-plane) position of the slide stage on the laser microscope. Minor adjustments to the positioning of the annotations over the tissue ROI can also be made during this step, if needed.
A limitation of the current version of the Malleator algorithm is that it is not compatible with the predefined annotation shape tools provided by the image analysis software (see the Table of Materials), although future updates/versions of the algorithm will aim to improve this compatibility. The .annotation file for shapes drawn using these tools contains only two sets of paired x and y coordinates for each annotation, without the complete spatial orientation around those points. Current use of these tools results in the annotations being converted to straight lines defined by only two points during the import process. Manual definition of tissue ROI segments is required for successful conversion to XML format and LMD import. This can either be performed by manually defining each ROI with individual free-hand polygonal annotations specific to the target area or by applying an approximated circular or rectangular annotation across all tissue ROI segments, if desired, and will be compatible with this workflow.
While the workflow presented here was demonstrated for proteomic analysis of fresh-frozen human cancer tissue specimens, this AI-driven LMD workflow can be equivalently used with FFPE tissues, non-cancerous tissue types, and those from non-human sources. It can also support other downstream molecular profiling workflows, including transcriptomic, genomic, or phosphoproteomic analyses. This workflow can also leverage other uses of the image analysis software (see the Table of Materials), including with capabilities associated with cell counting or other analytical modules, including the &#34;Multiplex IHC&#34; module or the &#34;Tissue Microarray (TMA) Add-on&#34;. Future applications of this workflow may also benefit from predefining the number of cells per ROI segment, thereby ensuring equivalent cellular inputs across multiple collections, or by using alternative methods to define cellular ROIs of interest, such as by immunohistochemistry or cell sociology.

The TME represents a complex ecosystem populated by a highly diverse array of cell types, such as tumor cells, stromal cells, immune cells, endothelial cells, other mesenchymal cell types, and adipocytes, along with a complex extracellular matrix1. This cellular ecosystem varies within and across different disease organ sites, resulting in complex tumor heterogeneity2,3. Recent studies have shown that heterogeneous tumors and tumors with low tumor cellularity (low purity) often correlate with poor disease prognosis2,3.
To understand the molecular interplay between tumor and non-tumor cell populations within the TME at scale, standardized and high-throughput strategies are necessary to selectively harvest distinct cellular populations of interest for downstream multiomic analysis. Quantitative proteomics represents a rapidly evolving and increasingly important technique to further the understanding of cancer biology. To date, the preponderance of studies employing proteomics have done so with proteins extracted from whole tumor tissue preparations (e.g., cryopulverized), leading to a paucity in the understanding of proteome-level heterogeneity in the TME4,5,6.
The development of sample collection strategies that seamlessly integrate with and harness information from clinical pathology workflows will enable a new generation of histology-resolved proteomics that are highly complementary to gold-standard, diagnostic pathology workflows. LMD enables direct and selective collection of cellular subpopulations or regions of interest (ROIs) through microscopic inspection of histologically stained tissue thin sections7. Recent major advances in digital pathology and AI-enabled analysis have demonstrated the ability to identify unique compositional features and ROIs within the TME in an automated fashion, many of which correlate with molecular alterations and clinical disease features, such as resistance to therapy and disease prognosis8.
The workflow described in the protocol presented here leverages commercial software solutions to selectively annotate tumor ROIs within digital histopathology images, and utilizes software tools developed in-house to transfer these tumor ROIs to laser microscopes for automated collection of discrete cellular populations of interest that seamlessly integrates with downstream multiomic analysis workflows. This integrated strategy significantly diminishes LMD operator time and minimizes the duration for which tissues are required to be at ambient temperature. The integration of automated feature selection and LMD harvest with high-throughput quantitative proteomics is demonstrated through a differential analysis of the TME from two representative epithelial ovarian cancer histologic subtypes, high-grade serous ovarian cancer (HGSOC) and ovarian clear cell carcinoma (OCCC).

<table><tbody><tr><td>1260 Infinity II System</td><td>Agilent Technologies Inc</td><td></td><td>Offline LC system</td></tr><tr><td>96 MicroCaps (150uL) in bulk</td><td>Pressure Biosciences Inc</td><td>MC150-96</td><td></td></tr><tr><td>96 MicroPestles in bulk</td><td>Pressure Biosciences Inc</td><td>MP-96</td><td></td></tr><tr><td>96 MicroTubes in bulk (no caps)</td><td>Pressure Biosciences Inc</td><td>MT-96</td><td></td></tr><tr><td>9mm MS Certified Clear Screw Thread Kits</td><td>Fisher Scientific</td><td>03-060-058</td><td>Sample vial for offline LC frationation and mass spectrometry</td></tr><tr><td>Acetonitrile, Optima LC/MS Grade</td><td>Fisher Chemical</td><td>A995-4</td><td>Mobile phase solvent</td></tr><tr><td>Aperio AT2</td><td>Leica Microsystems</td><td>23AT2100</td><td>Slide scanner</td></tr><tr><td>Axygen&amp;nbsp;PCR Tubes with 0.5 mL Flat Cap</td><td>Fisher Scientific</td><td>14-222-292</td><td>Sample tubes; size fits PCT tubes and thermocycler</td></tr><tr><td>Barocycler 2320EXT</td><td>Pressure Biosciences Inc</td><td>2320-EXT</td><td>Barocycler</td></tr><tr><td>BCA Protein Assay Kit</td><td>Fisher Scientific</td><td>P123225</td><td></td></tr><tr><td>cOmplete, Mini, EDTA-free Protease Inhibitor Cocktail</td><td>Roche</td><td>11836170001</td><td></td></tr><tr><td>Easy-nLC 1200</td><td>Thermo Fisher Scientific</td><td></td><td>Liquid Chromatography&amp;nbsp;</td></tr><tr><td>EasyPep Maxi Sample Prep Kit</td><td>Thermo Fisher Scientific</td><td>NCI5734</td><td>Post-label sample clean up column</td></tr><tr><td>EASY-SPRAY C18 2UM 50CM X 75</td><td>Fisher Scientific</td><td>ES903</td><td>Analytical column</td></tr><tr><td>Eosin Y Solution Aqueous</td><td>Sigma Aldrich</td><td>HT110216</td><td></td></tr><tr><td>Formic Acid, 99+ %</td><td>Thermo Fisher Scientific</td><td>28905</td><td>Mobile phase additive</td></tr><tr><td>ggplot2 version 3.3.5</td><td>CRAN</td><td>https://cran.r-project.org/web/packages/ggplot2/</td><td></td></tr><tr><td>HALO</td><td>Indica Labs</td><td></td><td>Image analysis software</td></tr><tr><td>IDLE&amp;nbsp;(Integrated Development and Learning Environment)</td><td>Python Software Foundation</td><td></td><td></td></tr><tr><td>iheatmapr version 0.5.1</td><td>CRAN</td><td>https://cran.r-project.org/web/packages/iheatmapr/</td><td></td></tr><tr><td>iRT Kit</td><td>Biognosys</td><td>Ki-3002-1</td><td>LC-MS QAQC Standard</td></tr><tr><td>limma version 3.42.2</td><td>Bioconductor</td><td>https://bioconductor.org/packages/release/bioc/html/limma.html</td><td></td></tr><tr><td>LMD Scanning stage Ultra LMT350</td><td>Leica Microsystems</td><td>11888453</td><td>LMD stage model outfitted with PCT tube holder</td></tr><tr><td>LMD7 (software version 8.2.3.7603)</td><td>Leica Microsystems</td><td></td><td>LMD apparatus (microscope, laser, camera, PC, tablet)</td></tr><tr><td>Mascot Server</td><td>Matrix Science</td><td></td><td>Data analysis software</td></tr><tr><td>Mass Spec-Compatible Human Protein Extract, Digest</td><td>Promega</td><td>V6951</td><td>LC-MS QAQC Standard</td></tr><tr><td>Mayer&amp;rsquo;s Hematoxylin Solution</td><td>Sigma Aldrich</td><td>MHS32</td><td></td></tr><tr><td>PEN Membrane Glass Slides</td><td>Leica Microsystems</td><td>11532918</td><td></td></tr><tr><td>Peptide Retention Time Calibration Mixture</td><td>Thermo Fisher Scientific</td><td>88321</td><td>LC-MS QAQC Standard</td></tr><tr><td>Phosphatase Inhibitor Cocktail 2</td><td>Sigma Aldrich</td><td>P5726</td><td></td></tr><tr><td>Phosphatase Inhibitor Cocktail 3</td><td>Sigma Aldrich</td><td>P0044</td><td></td></tr><tr><td>Pierce LTQ Velos ESI Positive Ion Calibration Solution</td><td>Thermo Fisher Scientific</td><td>88323</td><td>Instrument calibration solution</td></tr><tr><td>PM100 C18 3UM 75UMX20MM NV 2PK</td><td>Fisher Scientific</td><td>164535</td><td>Pre-column</td></tr><tr><td>Proteome Discoverer</td><td>Thermo Fisher Scientific</td><td>OPTON-31040</td><td>Data analysis software</td></tr><tr><td>Python</td><td>Python Software Foundation</td><td></td><td></td></tr><tr><td>Q Exactive HF-X</td><td>Thermo Fisher Scientific</td><td></td><td>Mass spectrometer</td></tr><tr><td>R version 3.6.0</td><td>CRAN</td><td>https://cran-archive.r-project.org/bin/windows/base/old/2.6.2/</td><td></td></tr><tr><td>RColorBrewer version 1.1-2</td><td>CRAN</td><td>https://cran.r-project.org/web/packages/RColorBrewer/</td><td></td></tr><tr><td>Soluble Smart Digest Kit</td><td>Thermo Fisher Scientific</td><td>3251711</td><td>Digestion reagent</td></tr><tr><td>TMTpro 16plex Label Reagent Set</td><td>Thermo Fisher Scientific</td><td>A44520</td><td>isobaric TMT labeling reagents</td></tr><tr><td>Veriti 60 well thermal cycler</td><td>Applied Biosystems</td><td>4384638</td><td>Thermocycler</td></tr><tr><td>Water, Optima LC/MS Grade</td><td>Fisher Chemical</td><td>W6-4</td><td>Mobile phase solvent</td></tr><tr><td>ZORBAX Extend 300 C18, 2.1 x 12.5 mm, 5 &amp;micro;m, guard cartridge (ZGC)</td><td>Agilent Technologies Inc</td><td>821125-932</td><td>Offline LC trap column</td></tr><tr><td>ZORBAX Extend 300 C18, 2.1 x 150 mm, 3.5 &amp;micro;m</td><td>Agilent Technologies Inc</td><td>763750-902</td><td>Offline LC analytical column</td></tr></tbody></table>

industrialized, artificial intelligence-guided laser microdissection for microscaled proteomic analysis of the tumor microenvironment

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell populations. To characterize proteome-level variation and tumor heterogeneity at scale, high-throughput methods are needed to selectively isolate discrete cellular populations in solid tumor malignancies. This protocol describes a high-throughput workflow, enabled by artificial intelligence (AI), that segments images of hematoxylin and eosin (H&#38;E)-stained, thin tissue sections into pathology-confirmed regions of interest for selective harvest of histology-resolved cell populations using laser microdissection (LMD). This strategy includes a novel algorithm enabling the transfer of regions denoting cell populations of interest, annotated using digital image software, directly to laser microscopes, thus enabling more facile collections. Successful implementation of this workflow was performed, demonstrating the utility of this harmonized method to selectively harvest tumor cell populations from the TME for quantitative, multiplexed proteomic analysis by high-resolution mass spectrometry. This strategy fully integrates with routine histopathology review, leveraging digital image analysis to support enrichment of cellular populations of interest and is fully generalizable, enabling harmonized harvests of cell populations from the TME for multiomic analyses.

Fresh-frozen tissue thin sections from two HGSOC and two OCCC patients were analyzed using this integrated AI-driven tissue ROI identification, segmentation, LMD, and quantitative proteomic analysis workflow (Figure 1). Representative H&#38;E-stained tissue sections for each tumor were reviewed by a board-certified pathologist; tumor cellularity ranged from 70% to 99%. Tissues were thin-sectioned onto PEN membrane slides (Supplemental File 2) and precut with calibrator fiducials (Supplemental File 1), enabling integration of positional orientation data from annotations generated in the image analysis software (see the Table of Materials) with Cartesian coordinate orientation in the LMD software. Following H&#38;E staining, high-resolution images (20x) of the PEN slides containing the tissue plus calibrators were captured.
Tumor and stromal cell populations in the micrographs were segmented using image analysis software (see the Table of Materials) for selective harvest by LMD, along with harvests representing the entire tissue thin section (e.g., whole tumor tissue) (Figure 1). Non-discriminate annotations for whole tumor tissue collections were generated by partitioning the entire tissue section with tiles of 500 &#181;m2, leaving a 40 &#181;m gap between tiles to maintain PEN membrane integrity and prevent the membrane from curling during LMD. On slides for histology-resolved LMD enrichment, the AI classifier in the image analysis software (see the Table of Materials) was trained to discriminate between tumor and stromal cells, along with the blank glass slide background. Representative tumor, stroma, and blank glass regions were manually highlighted, and the classifier tool was used to segment these ROIs throughout the entire tissue section. The segmented layers representing whole tissue, tumor epithelium, and stroma were saved separately as individual .annotation files (Supplemental File 3 and Supplemental File 6). In a separate copy of the image file (without the partitioned ROI annotations), a short line from the centermost tip of each of the three fiducial calibrators was annotated and saved as a .annotation file using the same file name as each of the LMD annotation layer files but appended with the suffix &#34;_calib&#34; (Supplemental File 4). These lines were used to co-register the position of the PEN membrane calibrators with the annotation shape list data drawn in the image analysis software.
The present study provides two algorithms, &#34;Malleator&#34; and &#34;Dap&#7885;&#34; in Python to support this AI-driven LMD workflow, which are available at https://github.com/GYNCOE/Mitchell.et.al.2022. The Malleator algorithm extracts the specific Cartesian coordinates for all individual annotations (tissue ROI and calibrators) from the paired .annotation files and merges these into a single Extensible Markup Language (XML) import file (Supplemental File 5). Specifically, the Malleator algorithm uses the directory name from a parent folder as input to search all subdirectory folders and generates .xml files for any subfolders that do not already have a .xml merged file. The Malleator algorithm merges all annotation layers in the image analysis software (see the Table of Materials) into a single layer and converts the AI-generated shape list data, which is saved as proprietary .annotation file type, into .xml format compatible with the LMD software. After merging the annotation and calibrator files, the algorithm-generated .xml file is saved and imported into the LMD software. Slight adjustments are necessary to manually adjust the alignment of annotations, which also serves to register the vertical (z-plane) position of the slide stage on the laser microscope. The Dap&#7885; algorithm is used specifically for LMD-enriched collections. Partitioned tiles are automatically assigned to individual annotation layers by the image analysis software. The Dap&#7885; algorithm merges all partitioned tiles into a single annotation layer prior to use of the Classifier tool, thereby reducing the Classifier analysis run time for LMD enriched collections.
The whole tumor and LMD-enriched tissue samples were digested, labeled with TMT reagents, multiplexed, fractionated offline, and analyzed via quantitative MS-based proteomics as previously described9. The mean peptide yield (43-60 &#181;g) and recovery (0.46-0.59 &#181;g/mm2) for samples harvested using this AI-driven workflow were comparable with previous reports9,10. A total of 5,971 proteins were co-quantified across all samples (Supplemental Table S1). Unsupervised hierarchical clustering using the 100 most variable proteins resulted in segregation of the HGSOC and OCCC histotypes from the LMD-enriched and whole tumor samples (Figure 2A), similar to that previously described11. By contrast, the LMD-enriched stroma samples from both HGSOC and OCCC clustered together and independently from the LMD-enriched tumor and whole tumor samples. Among the 5,971 quantified proteins, 215 were significantly altered (LIMMA adj. p &#60; 0.05) between whole tumor collections from HGSOC and OCCC specimens (Supplemental Table S2). These altered proteins were compared with those identified to differentiate HGSOC and OCCC tumor tissue by Hughes et al.11. Of the 76 signature proteins quantified by Hughes et al., 57 were co-quantified in this dataset and were highly correlated (Spearman Rho = 0.644, p &#60; 0.001) (Figure 2B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64171/64171fig01.jpg" /> 
Figure 1: Summary of the integrated workflow for automated tissue region of interest selection for laser microdissection for downstream quantitative proteomics. Calibration fiducials are cut onto PEN membrane slides to co-register positional orientation data from AI-derived segments of tissue ROI in the image analysis software, HALO, with horizontal positioning on the LMD microscope. The Malleator algorithm is used to merge the annotated segmentation data across all annotation layers for a slide with the _calib reference file, and to convert it to a .xml file compatible with the LMD software. LMD-harvested tissue for proteomic analysis is digested and analyzed by high-throughput quantitative proteomics as previously described9. Abbreviations: LMD = laser microdissection; ROI = region of interest; TMT = tandem mass tag; Quant. = quantification; Ident. = identification; LC-MS/MS = liquid chromatography-tandem mass spectrometry. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/64171fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64171/64171fig02.jpg" /> 
Figure 2: Analysis of the proteins in LMD-enriched and whole tumor samples. (A) Unsupervised hierarchical cluster analysis of the 100 most variably abundant proteins in HGSOC and OCCC LMD enriched and whole tumor samples. (B) Correlation of log2 fold-change protein abundances between HGSOC and OCCC whole tumor harvests in the present study (Mitchell et al., x-axis) and a similar study by Hughes et al. (y-axis)11. Abbreviations: LMD = laser microdissection; HGSOC = high-grade serous ovarian cancer; OCCC = ovarian clear cell carcinoma; log2FC = log2-transformed proteomic abundance. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/64171fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Table S1: Abundances of 5,971 proteins co-quantified across all LMD enriched and whole tumor samples from HGSOC and OCCC tissue specimens. Abbreviations: LMD = laser microdissection; HGSOC = high-grade serous ovarian cancer; OCCC = ovarian clear cell carcinoma. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/JoVE64171_Supplemental Table 1 JoVE Review.zip" target="_blank">Please click here to download this Table.</a>
Supplemental Table S2: Differentially expressed proteins (215) in whole tumor collections from HGSOC vs OCCC (LIMMA adj. p &#60; 0.05). Abbreviations: HGSOC = high-grade serous ovarian cancer; OCCC = ovarian clear cell carcinoma. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/JoVE_Supplemental Table 2_4 May 22 (1).zip" target="_blank">Please click here to download this Table.</a>
Supplemental File 1: Representative shape list data (.sld) file containing standard calibrator fiducials for four slide positions. The file can be imported into the LMD software. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/Supplemental File 1_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 2: Representative .svs image file of a H&#38;E-stained high-resolution (20x) tissue section. The file can be opened and viewed using image analysis software or LMD software. Abbreviation: H&#38;E = hematoxylin and eosin; LMD = laser microdissection. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/Supplemental JoVE64171_Supplemental File 2.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 3: Representative .annotation file of partitioned whole tumor segments. The file can be imported into image analysis software. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/Supplemental File 3_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 4: Representative _calib.annotation file of calibrator fiducial segments. Coordinate information represents oriental positioning of the short calibrator lines drawn from each arrowhead fiducial. The file can be imported into image analysis software. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/Supplemental File 4_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 5: Representative extensible markup language (.xml) file generated by the Malleator algorithm. The file can be imported into the laser microdissection software. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/Supplemental File 5_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 6: Representative .annotation file of partitioned AI-classified segments for LMD-enriched collections. The file can be imported into image analysis software. Abbreviations: AI = artificial intelligence; LMD = laser microdissection. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64171/Supplemental File 6_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment at JoVE.com

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

This protocol describes a high-throughput workflow for artificial intelligence-driven segmentation of pathology-confirmed regions of interest from stained, thin tissue section images for enrichment of histology-resolved cell populations using laser microdissection. This strategy includes a novel algorithm enabling the transfer of demarcations denoting cell populations of interest directly to laser microscopes.

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell ...

industrialized-artificial-intelligence-guided-laser-microdissection

Nanyang Technological University

Research

JoVE Journal

Cancer Research

3.6K Views.  Uniformed Services University and Walter Reed National Military Medical Center. Dit protocol integreert AI-gestuurde beeldanalyse voor weefselsegmentatie. Met histologie selectieve oogst door laser microdissectie en brengt ons dichter bij de realiteit van Pathomics. Het vooraf definiëren van weefsel ROI met behulp van AI, beperkt de verblijftijd van weefselglijbanen bij omgevingstemperatuur, vermindert de inspanning van de mankracht om deze oogsten uit te voeren en vermindert de variabiliteit van de operator.Deze techniek is breed toepasbaar op elk op ziekte of p...

Video: Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Laser Capture Microdissection of Mammalian Tissue

Laser capture microscopy, also known as laser microdissection (LMD), enables the user to isolate small numbers of cells or tissues from frozen or formalin-fixed, paraffin-embedded tissue sections. LMD techniques rely on a thermo labile membrane placed either on top of, or underneath, the tissue section. In one method, focused laser energy is used to melt the membrane onto the underlying cells, which can then be lifted out of the tissue section. In the other, the laser energy vaporizes the foil along a path "drawn" on the tissue, allowing the selected cells to fall into a collection device. Each technique allows the selection of cells with a minimum resolution of several microns. DNA, RNA, protein, and lipid samples may be isolated and analyzed from micro-dissected samples. In this video, we demonstrate the use of the Leica AS-LMD laser microdissection instrument in seven segments, including an introduction to the principles of LMD, initializing the instrument for use, general considerations for sample preparation, mounting the specimen and setting up capture tubes, aligning the microscope, adjusting the capture controls, and capturing tissue specimens. Laser-capture micro-dissection enables the investigator to isolate samples of pure cell populations as small as a few cell-equivalents. This allows the analysis of cells of interest that are free of neighboring contaminants, which may confound experimental results.

Laser capture microscopy, also known as laser microdissection (LMD), enables the user to isolate small numbers of cells or tissues from frozen or ...

Biology

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly with many downstream assays such as next generation sequencing. Automated tissue dissection is a new methodology that automates and improves tumor enrichment in these common, low tumor content tissues by decreasing the user-dependent imprecision of traditional macro-dissection and time, cost, and expertise limitations of laser capture microdissection by using digital image annotation overlay onto unstained slides. Here, digital hematoxylin and eosin (H&#38;E) annotations are used to target small tumor areas using a blade that is 250 &#181;m2 in diameter in unstained formalin fixed paraffin embedded (FFPE) or fresh frozen sections up to 20 &#181;m in thickness for automated tumor enrichment prior to nucleic acid extraction and whole exome sequencing (WES). Automated dissection can harvest annotated regions in low tumor content tissues from single or multiple sections for nucleic acid extraction. It also allows for capture of extensive pre- and post-harvest collection metrics while improving accuracy, reproducibility, and increasing throughput with utilization of fewer slides. The described protocol enables digital annotation with automated dissection on animal and/or human FFPE or fresh frozen tissues with low tumor content and could also be used for any region of interest enrichment to boost adequacy for downstream sequencing applications in clinical or research workflows.

Digital annotation with automated tissue dissection provides an innovative approach to enriching tumor in low tumor content cases and is adaptable to both paraffin and frozen tissue types. The described workflow improves accuracy, reproducibility and throughput and could be applied to both research and clinical settings.

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly ...

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein and RNA multiplexing by leveraging photo-cleavable oligo-tagged antibodies and probes, respectively. Oligos are cleaved and quantified from specific regions across the tissue to elucidate the underlying biology. Here, the study demonstrates that automated custom antibody visualization protocols can be utilized to guide ROI selection in conjunction with spatial proteomics assays. This specific method did not show acceptable performance with spatial transcriptomics assays. The protocol describes the development of a 3-plex immunofluorescent (IF) assay for marker visualization on an automated platform, using tyramide signal amplification (TSA) to amplify the fluorescent signal from a given protein target and increase the antibody pool to choose from. The visualization protocol was automated using a thoroughly validated 3-plex assay to ensure quality and reproducibility. In addition, the exchange of DAPI for SYTO dyes was evaluated to allow imaging of TSA-based IF assays on the spatial profiling platform. Additionally, we tested the ability of selecting small ROIs using the spatial transcriptomics assay to allow the investigation of highly-specific areas of interest (e.g., areas enriched for a given cell type). ROIs of 50 &#181;m and 300 &#181;m diameter were collected, which corresponds to approximately 15 cells and 100 cells, respectively. Samples were made into libraries and sequenced to investigate the capability to detect signals from small ROIs and profile-specific regions of the tissue. We determined that spatial proteomics technologies highly benefit from automated, standardized protocols to guide ROI selection. While this automated visualization protocol was not compatible with spatial transcriptomics assays, we were able to test and confirm that specific cell populations can successfully be detected even in small ROIs with the standard manual visualization protocol.

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 &#181;m.

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein ...

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Invading colorectal cancer (CRC) cells have acquired the capacity to break free from their sister cells, infiltrate the stroma, and remodel the extracellular matrix (ECM). Characterizing the biology of this phenotypically distinct group of cells could substantially improve our understanding of early events during the metastatic cascade.

Tumor invasion is a dynamic process facilitated by bidirectional interactions between malignant epithelium and the cancer associated stroma. In order to examine cell-specific responses at the tumor stroma-interface we have combined organotypic co-culture and laser micro-dissection techniques.
Organotypic models, in which key stromal constituents such as fibroblasts are 3-dimentioanally co-cultured with cancer epithelial cells, are highly manipulatable experimental tools which enable invasion and cancer-stroma interactions to be studied in near-physiological conditions.

Laser microdissection (LMD) is a technique which entails the surgical dissection and extraction of the various strata within tumor tissue, with micron level precision.
By combining these techniques with genomic, transcriptomic and epigenetic profiling we aim to develop a deeper understanding of the molecular characteristics of invading tumor cells and surrounding stromal tissue, and in doing so potentially reveal novel biomarkers and opportunities for drug development in CRC.&#160;&#160;&#160;

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Molecular profiling of laser microdissected cells and stroma from synthetic, tissue-engineered colorectal cancer models represents a novel and manipulatable approach to characterize and dissect the distinctive biology at the interface between tumor cells at the invasive front and cancer associated stromal cells. &#160;

Invading colorectal cancer (CRC) cells have acquired the capacity to break free from their sister cells, infiltrate the stroma, and remodel the ...

Medicine

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.

Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. ...

Bioengineering

Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis

The prostate gland contains a heterogeneous milieu of stromal, epithelial, neuroendocrine and immune cell types. Healthy prostate is comprised of fibromuscular stroma surrounding discrete epithelial-lined secretory lumens and a very small population of immune and neuroendocrine cells. In contrast, areas of prostate cancer have increased dysplastic luminal epithelium with greatly reduced or absent stromal population. Given the profound difference between stromal and epithelial cell types, it is imperative to separate the cell types for any type of downstream molecular analysis. Despite this knowledge, the bulk of gene expression studies compare benign prostate to cancer without micro-dissection, leading to stromal bias in the benign samples. Laser-capture micro-dissection (LCM) is an effective method to physically separate different cell types from a specimen section. The goal of this protocol is to show that RNA can be successfully isolated from LCM-collected human prostatic epithelium and used for downstream gene expression studies such as RT-qPCR and RNAseq.

The goal of this protocol is to use laser-capture micro-dissection as an effective method to isolate pure populations of cell types from heterogeneous prostate tissues for downstream RNA analysis.

The prostate gland contains a heterogeneous milieu of stromal, epithelial, neuroendocrine and immune cell types.  Healthy prostate is comprised of ...

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Gliomas are primary brain tumors characterized by their invasiveness and heterogeneity. Specific histological patterns such as pseudopalisades, microvascular proliferation, mesenchymal transformation and necrosis characterize the histological heterogeneity of high-grade gliomas. Our laboratory has demonstrated that the presence of high densities of mesenchymal cells, named oncostreams, correlate with tumor malignancy. We have developed a unique approach to understand the mechanisms that underlie glioma&#39;s growth and invasion. Here, we describe a comprehensive protocol that utilizes laser capture microdissection (LMD) and RNA sequencing to analyze differential mRNA expression of intra-tumoral heterogeneous multicellular structures (i.e., mesenchymal areas or areas of tumor invasion). This method maintains good tissue histology and RNA integrity. Perfusion, freezing, embedding, sectioning, and staining were optimized to preserve morphology and obtain high-quality laser microdissection samples. The results indicate that perfusion of glioma bearing mice using 30% sucrose provides good morphology and RNA quality. In addition, staining tumor sections with 4% Cresyl violet and 0.5% eosin results in good nuclear and cellular staining, while preserving RNA integrity. The method described is sensitive and highly reproducible and it can be utilized to study tumor morphology in various tumor models. In summary, we describe a complete method to perform LMD that preserves morphology and RNA quality for sequencing to study the molecular features of heterogeneous multicellular structures within solid tumors.

Laser microdissection (LMD) is a sensitive and highly reproducible technique that can be used to uncover pathways that mediate glioma heterogeneity and invasion. Here, we describe an optimized protocol to isolate discrete areas from glioma tissue using laser LMD followed by transcriptomic analysis.

Gliomas are primary brain tumors characterized by their invasiveness and heterogeneity. Specific histological patterns such as pseudopalisades, ...

Neuroscience

SIVQ-LCM Protocol for the ArcturusXT Instrument

SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process.&#160;It aims to create an advanced, rapidly customizable laser dissection platform technology. In this report, we describe the integration of the image analysis software Spatially Invariant Vector Quantization (SIVQ) onto the ArcturusXT instrument. The ArcturusXT system contains both an infrared (IR) and ultraviolet (UV) laser, allowing for specific cell or large area dissections. The principal goal is to improve the speed, accuracy, and reproducibility of the laser dissection to increase sample throughput. This novel approach facilitates microdissection of both animal and human tissues in research and clinical workflows.

SIVQ-LCM is an innovative approach that harnesses a computer algorithm, Spatially Invariant Vector Quantization (SIVQ), to drive the laser capture microdissection (LCM) process. The SIVQ-LCM workflow greatly improves the speed and accuracy of microdissection, with applications in both the research and clinical settings.

SIVQ-LCM is a new methodology that automates and streamlines the more traditional, user-dependent laser dissection process.&#160;It aims to create an ...

Laser Microdissection-Based Protocol for the LC-MS/MS Analysis of the Proteomic Profile of Neuromelanin Granules

Neuromelanin is a black-brownish pigment, present in so-called neuromelanin granules (NMGs) in dopaminergic neurons of the substantia nigra pars compacta. Besides neuromelanin, NMGs contain a variety of proteins, lipids, and metals. Although NMGs-containing dopaminergic neurons are preferentially lost in neurodegenerative diseases like Parkinson&#39;s disease and dementia with Lewy bodies, only little is known about the mechanism of NMG formation and the role of NMGs in health and disease. Thus, further research on the molecular characterization of NMGs is essential. Unfortunately, standard protocols for the isolation of proteins are based on density gradient ultracentrifugation and therefore require high amounts of human tissue. Thus, an automated laser microdissection (LMD)-based protocol is established here which allows the collection of NMGs and surrounding substantia nigra (SN) tissue using minimal amounts of tissue in an unbiased, automatized way. Excised samples are subsequently analyzed by mass spectrometry to decipher their proteomic composition. With this workflow, 2,079 proteins were identified of which 514 proteins were exclusively identified in NMGs and 181 in SN. The present results have been compared with a previous study using a similar LMD-based approach reaching an overlap of 87.6% for both proteomes, verifying the applicability of the revised and optimized protocol presented here. To validate current findings, proteins of interest were analyzed by targeted mass spectrometry, e.g., parallel reaction monitoring (PRM)-experiments.

A robust protocol is presented here for isolating neuromelanin granules from human post-mortem substantia nigra pars compacta tissue via laser microdissection. This revised and optimized protocol massively minimizes the required time for sample collection, reduces the required sample amount, and enhances the identification and quantification of proteins by LC-MS/MS analysis.

Neuromelanin is a black-brownish pigment, present in so-called neuromelanin granules (NMGs) in dopaminergic neurons of the substantia nigra pars compacta. ...

Laser Microdissection for Species-Agnostic Single-Tissue Applications

Single-cell methodologies have revolutionized the analysis of the transcriptomes of specific cell types. However, they often require species-specific genetic &#34;toolkits,&#34; such as promoters driving tissue-specific expression of fluorescent proteins. Further, protocols that disrupt tissues to isolate individual cells remove cells from their native environment (e.g., signaling from neighbors) and may result in stress responses or other differences from native gene expression states. In the present protocol, laser microdissection (LMD) is optimized to isolate individual nematode tail tips for the study of gene expression during male tail tip morphogenesis.
LMD allows the isolation of a portion of the animal without the need for cellular disruption or species-specific toolkits and is thus applicable to any species. Subsequently, single-cell RNA-seq library preparation protocols such as CEL-Seq2 can be applied to LMD-isolated single tissues and analyzed using standard pipelines, given that a well-annotated genome or transcriptome is available for the species. Such data can be used to establish how conserved or different the transcriptomes are that underlie the development of that tissue in different species.
Limitations include the ability to cut out the tissue of interest and the sample size. A power analysis shows that as few as 70 tail tips per condition are required for 80% power. Tight synchronization of development is needed to obtain this number of animals at the same developmental stage. Thus, a method to synchronize animals at 1 h intervals is also described.

A protocol is described that uses laser microdissection to isolate individual nematode tissues for RNA-sequencing. The protocol does not require species-specific genetic toolkits, allowing gene expression profiles to be compared between different species at the level of single-tissue samples.

Single-cell methodologies have revolutionized the analysis of the transcriptomes of specific cell types. However, they often require species-specific ...

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

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